Systems and methods for evaluating models that generate recommendations

ABSTRACT

A device may receive content data, a first model, and a second model. The first model may be trained on different types of metadata than the second model. The content data may include a first identifier of a first content item and a first set of metadata associated with the first content item. The device may process the first set of metadata to generate first recommendations from the first model and second recommendations from the second model. The device may provide the first identifier and a combination of the first recommendations and the second recommendations to client devices. The device may receive, from the client devices, user-generated target recommendations based on the combination. The device may process the user-generated target recommendations, the first recommendations, and the second recommendations, to provide feedback to update the first model and the second model.

RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.16/922,460, filed Jul. 7, 2020, and entitled “SYSTEMS AND METHODS FOREVALUATING MODELS THAT GENERATE RECOMMENDATIONS” (now U.S. Pat. No.11,070,881), which is incorporated herein by reference in its entirety.

BACKGROUND

A content delivery system may provide content to client devices of aplurality of users. Based on selection of a content item (e.g., a movie,a television show, a song, a game, a book, and/or the like), by a userof the plurality of users, the content delivery system may generaterecommendations for the user.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1M are diagrams of one or more example implementationsdescribed herein.

FIG. 2 is a diagram illustrating an example of training and using amodel to generate recommendations

FIG. 3 is a diagram of an example environment in which systems and/ormethods described herein may be implemented.

FIG. 4 is a diagram of example components of one or more devices of FIG.3.

FIG. 5 is a flow chart of an example process relating to evaluatingmodels that generate recommendations.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

The following detailed description of example implementations refers tothe accompanying drawings. The same reference numbers in differentdrawings may identify the same or similar elements.

A content delivery system may provide content to client devices of aplurality of users. The content may include movies, television shows,songs, games, books, a combination thereof, and/or the like. Based onselection of a content item (e.g., a movie, a television show, a song, agame, a book, and/or the like) by a user of the plurality of users, thecontent delivery system may generate recommendations for the user. Thecontent delivery system may generate the recommendations based onmetadata associated with the content item. The metadata may identify,for example, a genre of the content item, one or more creators of thecontent item, a rating of the content item, a release year of thecontent item, and/or the like. Additionally, or alternatively, thecontent delivery system may generate the recommendations based on aselection history of the user, feedback from the user, a selectionhistory of one or more other users, feedback from the one or more otherusers, and/or the like. The recommendations may be presented to the useras a list of content items related to the content item. The list ofcontent items may be ranked in order of likelihood of interest to theuser.

When recommendations align with personal preferences of the user, therecommendations may be more valuable to a provider of the contentdelivery system, to the content creators, and to the user. However,because the content delivery system may have thousands or millions ofusers, optimizing the recommendations may be a difficult task. Thecontent delivery system may consume computing resources (e.g.,processing resources, memory resources, communication resources, and/orthe like) and/or network resources storing large amounts of datarelating to content selection, analyzing the large amounts of data,and/or the like.

Some implementations described herein provide a device (e.g., a testingplatform, and/or the like) that facilitates evaluation and improvementof models that generate recommendations. The device may receive contentdata, a first model for generating first recommendations, and a secondmodel for generating second recommendations. The content data mayinclude content identifiers of content items and sets of metadataassociated with the content items. The second model may be trained ondifferent types of metadata than the first model and/or trained using adifferent technique than the first model. The device may process, withinone or more testing modes, the sets of metadata associated with thecontent items, to generate the first recommendations from the firstmodel and the second recommendations from the second model. The one ormore testing modes may permit the device to obtain different forms ofuser feedback regarding the first recommendations and the secondrecommendations. The device may provide the content identifiers of thecontent items along with the first recommendations and the secondrecommendations to client devices for the user feedback. The device mayreceive, from the client devices, the user feedback that indicateswhether the first recommendations were more or less accurate than thesecond recommendations. Based on the user feedback, the device mayperform one or more actions to update the first model and/or the secondmodel.

By streamlining evaluation of the models, the device may ensure that therecommendations are optimized to align with preferences of the users.Furthermore, the device may conserve computing resources (e.g.,processing resources, memory resources, communication resources, and/orthe like) and/or network resources that might otherwise have beenconsumed storing large amounts of data relating to content selection,analyzing the large amounts of data, and/or the like.

FIGS. 1A-1M are diagrams of one or more example implementations 100described herein. FIG. 1A illustrates an example of a server device 105interacting with a testing platform 110 to configure the testingplatform 110 for evaluating models that generate recommendations. FIGS.1B-1L illustrate one or more examples of the testing platform 110interacting with client devices 115 to evaluate and/or enableimprovement of the models. FIG. 1M illustrates an example of the testingplatform 110 interacting with the server device 105 to cause the serverdevice 105 to implement a model within a system.

An online provider of goods and/or services (e.g., a content deliverysystem, a retailer, a rental service, a marketplace, a restaurant,and/or the like) may seek to utilize data to predict behavior of a userafter the user initially engages with a website of the online provider(e.g., by accessing an item and/or a service, by purchasing the itemand/or the service, by renting the item and/or the service, by movingthe item and/or service to a shopping cart, and/or the like). Using thedata, the online provider may train a plurality of models to generaterecommendations that are similar to the item and/or the service. Todetermine which of the plurality of models is more accurate, the onlineprovider may utilize the testing platform 110 to obtain feedback fromusers associated with client devices (e.g., the client devices 115). Toobtain the feedback, the users associated with the client devices 115may be invited to participate in a model evaluation process via email,upon registration with the website of the online provider, and/or thelike.

To simplify the explanation, FIGS. 1A-1M will be described below interms of a content delivery system that has two different models forgenerating recommendations of content items, such as movies, televisionshows, songs, games, books, products, a combination of the foregoing,and/or the like. It should be understood, however, that that the systemsand methods described in connection with FIGS. 1A-1M are provided merelyas examples. In practice, the systems and methods may be implemented fordifferent types of providers (e.g., online and/or brick and mortarretailers, rental services, marketplaces, restaurants, and/or the like),may utilize different types of data to train the models, may have morethan two models for generating recommendations, may generate differenttypes of recommendations, and/or the like.

In FIG. 1A, assume that a first model generator of the content deliverysystem generated a first model to provide recommendations based oncontent items. The first model generator may have trained the firstmodel based on one or more first types of metadata associated with thecontent items, selection history of the content items, user feedbackdata relating to the content items, and/or the like. The metadataassociated with a content item may include information identifying agenre of the content item, a creator of the content item, a rating ofthe content item, a release year of the content item, and/or the like.These types of metadata are intended to be examples of types of metadatathat might be used in a given context. In practice, any one of thesetypes of metadata, any combination of these types of metadata, or one ormore different types of metadata may be used.

The first model generator may have used one or more first techniques,such as a weighted variable analysis technique, a regression technique(e.g., a linear regression technique, a power function regressiontechnique, a logarithmic regression technique, and/or the like), arandom forest classification technique, a gradient boosting machinelearning (GBM) technique, and/or the like, to train the first model todetermine a score of a recommended content item (e.g., a probabilitythat a user will be interested in the recommended content item, and/orthe like). After generating the first model, the first model generatormay provide the first model to the server device 105.

In FIG. 1A, assume further that a second model generator of the contentdelivery system generated a second model, different from the firstmodel, to also provide recommendations based on the content items. Thesecond model generator may have trained the second model based on one ormore second types of metadata that are different than at least one ofthe one or more first types of metadata. For example, if the one or morefirst types of metadata include information identifying genre andcreators, the one or more second types of metadata may includeinformation identifying ratings and release year. As another example, ifthe one or more first types of metadata include information identifyinggenre, creators, and ratings, the one or more second types of metadatamay include information identifying genre, creators, and release year.

Additionally, or alternatively, the second model generator may have usedone or more second techniques, different than the one or more firsttechniques, to train the second model to determine a score of arecommended content item (e.g., a probability that a user will beinterested in the recommended content item, and/or the like). Forexample, if the first model generator trained the first model using aweighted variable analysis technique, the second model generator mayhave trained the second model using a regression technique (e.g., alinear regression technique, a power function regression technique, alogarithmic regression technique, and/or the like), a random forestclassification technique, a GBM technique, and/or the like. As anotherexample, if the first model generator used a weighted variable analysistechnique involving first weights (e.g., genre being weighted 50%,creators being weighted 40%, ratings being weighted 10%, and/or thelike), the second model generator may have used a weighted variableanalysis technique involving second weights that are different than thefirst weights (e.g., creators being weighted 30%, genre being weighted30%, ratings being weighted 30%, release year being weighted 10%, and/orthe like). After generating the second model, the second model generatormay provide the second model to the server device 105.

In FIG. 1A, assume further that the server device 105 has obtained andstored content data associated with the content delivery system. Thecontent data may include a plurality of content data sets associatedwith a plurality of content items. With respect to a content item, acontent data set may include an identifier of the content item (e.g., atitle of the content item, an image associated with the content item,and/or the like), metadata associated with the content item, and/or thelike.

As shown by reference number 120 in FIG. 1A, the server device 105 mayprovide the content data, the first model, and the second model to thetesting platform 110. The server device 105 may be configured to providethe content data, the first model, and the second model to the testingplatform 110 based on receiving a request from the testing platform 110,based on a configuration of the server device 105, based on receiving aninstruction from a user, and/or the like. In some implementations, theserver device 105 may transmit, and the testing platform 110 mayreceive, a link to the content data, a link to the first model, and/or alink to the second model. The testing platform 110 may use the links toobtain the content data, the first model, and/or the second model from astorage device. Additionally, or alternatively, the server device 105may transmit the content data, the first model, and/or the second modelto the testing platform 110 without transmitting a link for the contentdata, the first model, and/or the second model. Upon receipt of thecontent data, the first model, and/or the second model, the testingplatform 110 may store the content data, the first model, and/or thesecond model in one or more data structures for later access.

The testing platform 110 may be configured to evaluate the first modeland the second model within a plurality of testing modes, such as afirst testing mode, a second testing mode, and/or a third testing mode.In the first testing mode, which will be described in more detail belowin connection with FIGS. 1C-1E, the testing platform 110 may obtain andstore recommendations from the first model, which are arranged in afirst order, and recommendations from the second model, which arearranged in a second order. The testing platform 110 may combine therecommendations to form a combination of recommendations (e.g., in arandom order, an alphanumeric order, and/or the like). The testingplatform 110 may present the combination of recommendations for displayon a user interface. Users, via client devices (e.g., the client devices115), may select recommendations from the combination of recommendationsand arrange the selected recommendations in an order of interest to theusers. The testing platform 110, upon receipt of the selectedrecommendations in the order of interest to the users, may compare theorder of the selected recommendations with the first order ofrecommendations from the first model and the second order ofrecommendations from the second model. The testing platform 110 maydetermine that the recommendations more closely aligned with theselected recommendations are more accurate and thus indicative of abetter model.

In the second testing mode, which will be described in more detail belowin connection with FIGS. 1F-1H, the testing platform may presentrecommendations from the first model in one grouping for display on theuser interface and present recommendations from the second model inanother grouping for display on the user interface. Users, via clientdevices (e.g., the client devices 115), may select the one grouping orthe other grouping, based on which grouping is more accurate to theusers. The testing platform 110 may determine that the grouping moreoften selected by the users includes more accurate recommendations andis likewise indicative of a better model.

In the third testing mode, which will be described in more detail belowin connection with FIGS. 1I-1K, the testing platform may present asingle recommendation from the first model and a single recommendationfrom the second model for display on the user interface. Users, viaclient devices (e.g., the client devices 115), may select the singlerecommendation from the first model or the single recommendation fromsecond model, based on which recommendation is more accurate to theusers. The testing platform 110 may determine that the singlerecommendation more often selected by the users is a more accuraterecommendation and is thus indicative of a better model.

The three testing modes are intended to be examples of a quantity oftesting modes and types of testing modes that might be used in a givencontext. In practice, any one of these testing modes, any combination ofthese testing modes, or one or more different types of testing modes maybe used.

As shown by reference number 125A in FIG. 1B, the testing platform 110may select the first testing mode, the second testing mode, or the thirdtesting mode for testing the first model and the second model. In someimplementations, the testing platform 110 may be configured to operatein the first testing mode, the second testing mode, the third testingmode, or a combination thereof. For example, the testing platform 110may be configured to operate in a single testing mode (of the pluralityof testing modes), to operate in multiple testing modes (e.g., any twoor more of the plurality of testing modes), to operate in multipletesting modes in a particular order (e.g., operate in the first testingmode before the second testing mode, operate in the second testing modebefore the third testing mode, and/or the like), and/or the like. Insome implementations, an administrator of the testing platform 110 mayinput a command to the testing platform 110 to indicate, to the testingplatform 110, in which testing mode(s) the testing platform 110 is tooperate, and the testing platform 110 may select the testing mode(s)based on the command.

As shown by reference number 125B in FIG. 1B, the client devices 115 mayprovide, to the testing platform 110, a selection of the first testingmode, the second testing mode, or the third testing mode for testing thefirst model and the second model. In some implementations, the testingplatform 110 may provide information identifying the first testing mode,the second testing mode, and/or the third testing mode as options to aplurality of client devices, including the client devices 115, via auser interface associated with the testing platform 110. For example,after users of the plurality of client devices access the user interface(e.g., based on an invitation to participate in a model evaluationprocess, and/or the like), the user interface may display informationidentifying the first testing mode, the second testing mode, and/or thethird testing mode. The user interface may prompt the users to make aselection to begin evaluating different outputs of the first modeland/or the second model. The client devices 115 may provide theselection at the same time, at different times, over a time period,and/or the like.

Assume, for the example shown in FIG. 1C, that the testing platform 110or the client devices 115 provide the selection of the first testingmode. Upon selection of the first testing mode, the testing platform 110may access the one or more data structures and select (e.g., randomly,based on a particular order, and/or the like) a first content data setfrom the content data. The first content data set may include a firstidentifier of a first content item, a first set of metadata associatedwith the first content item, and/or the like. If the first model and thesecond model have been trained based on different types of metadata(e.g., the first model having been trained based on the one or morefirst types of metadata, the second model having been trained based onthe one or more second types of metadata, and/or the like), the firstset of metadata may include a first subset of metadata for input intothe first model and a second subset of metadata for input into thesecond model. In such a case, the first subset of metadata may includethe one or more first types of metadata associated with the firstcontent item, and the second subset of metadata may include the one ormore second types of metadata associated with the first content item.

In some implementations, after selection of the first testing mode, thetesting platform 110 may access the one or more data structures andselect (e.g., randomly, based on a particular order, and/or the like)one or more content data sets from the content data. The one or morecontent data sets may include the first content data set, which includesthe first identifier of the first content item and the first set ofmetadata. Based on the selection of the one or more content data sets,the testing platform 110 may provide one or more content identifiers,including the first identifier, to the client devices 115 for selection.For example, the testing platform 110 may display, on the userinterface, the first identifier of the first content item. In case usersare unfamiliar with the first content item or otherwise choose not toanalyze recommendations based on the first content item, the userinterface may include an option to request a different content item toanalyze different recommendations. In another example, the testingplatform 110 may display, on the user interface, a plurality of contentidentifiers, including the first identifier of the first content item.In this example, users may select any of the plurality of contentidentifiers to evaluate corresponding recommendations. For purposes ofexplanation, assume that the users associated with the client devices115 selected the first identifier of the first content item for furtherassessment.

As shown by reference number 130 in FIG. 1C, the testing platform 110may process, within the first testing mode, the first set of metadataassociated with the first content item, to generate firstrecommendations from the first model and second recommendations from thesecond model. For example, if the testing platform 110 selected thefirst content data set, the testing platform 110 may input the first setof metadata associated with the first content item (or, respectively,the first subset of metadata and the second subset of metadata) into thefirst model and the second model. As another example, if the usersassociated with the client devices 115 selected the first identifier ofthe first content item, the testing platform 110, based on theselection, may search the one or more data structures for the firstidentifier to identify the first set of metadata. The testing platform110 may input the first set of metadata associated with the firstcontent item (or, respectively, the first subset of metadata and thesecond subset of metadata) into the first model and the second model.After the first model and the second model process the first set ofmetadata (or, respectively, the first subset of metadata and the secondsubset of metadata), the first model may generate the firstrecommendations, and the second model may generate the secondrecommendations.

For example, assume that the first content item is a first movie, suchas “Men in Black.” The testing platform 110 may input the first set ofmetadata or the first subset of metadata into the first model. Based onthe first set of metadata or the first subset of metadata, the firstmodel may determine scores of recommended content items of the contentdata. Based on the scores, the first model may output the firstrecommendations (e.g., content items having the highest scores, contentitems having scores that satisfy a threshold, and/or the like), and thetesting platform 110 may arrange the first recommendations in a firstorder (e.g., from highest to lowest score, and/or the like). The firstrecommendations may include a first set of movies that are differentthan “Men in Black.” For example, assume that the first recommendations,arranged in the first order, are (1) “Independence Day,” (2) “Alien,”and (3) “Guardians of the Galaxy.”

The testing platform 110 may likewise input the first set of metadata orthe second subset of metadata associated with “Men in Black” into thesecond model. Based on the first set of metadata or the second subset ofmetadata, the second model may determine scores of recommended contentitems of the content data. Based on the scores, the second model mayoutput the second recommendations (e.g., content items having thehighest scores, having scores that satisfy a threshold, and/or thelike), and the testing platform 110 may arrange the secondrecommendations in a second order (e.g., from highest to lowest score,and/or the like). The second recommendations may include a second set ofmovies that are different than “Men in Black.” For example, assume thatthe second recommendations, arranged in the second order, are (1) “Menin Black II,” (2) “I am Legend,” and (3) “Independence Day.”

After the testing platform 110 respectively arranges the firstrecommendations in the first order and the second recommendations in thesecond order, the testing platform 110 may store the firstrecommendations in the first order and the second recommendations in thesecond order for later assessment. To avoid biasing user feedback (e.g.,by providing users associated with the client devices 115 with the firstorder and the second order), the testing platform 110 may combine thefirst recommendations with the second recommendations to form acombination of the first recommendations and the second recommendations.The testing platform 110 may eliminate any duplicate instances ofrecommendations (e.g., “Independence Day”) and arrange the firstrecommendations and the second recommendations in a random order, analphanumeric order, and/or the like.

As shown by reference number 135 in FIG. 1D, the testing platform 110may provide the first identifier of the first content item and thecombination of the first recommendations and the second recommendationsto the client devices 115. For example, the user interface may displayinformation regarding the first movie, “Men in Black,” along with“Independence Day,” “Alien,” “Guardians of the Galaxy,” “Men in BlackII,” and “I am Legend” as proposed recommendations. The user interfacemay prompt the users associated with the client devices 115 to selectmovies from the proposed recommendations and arrange the movies in adesired order to define user-generated target recommendations. Inresponse, for example, a user may select, in order of interest, (1) “Menin Black II,” (2) “Independence Day,” and (3) “I am Legend.” Other usersmay select different movies, a different number of movies, arrangemovies in different orders, and/or the like. Once the users determinethe user-generated target recommendations, the users may submit theuser-generated target recommendations via the client devices 115. Thus,as shown by reference number 140, the client devices 115 may provide theuser-generated target recommendations to the testing platform 110.

As shown by reference number 145 in FIG. 1E, the testing platform 110may process, within the first testing mode, the user-generated targetrecommendations, the first recommendations, and the secondrecommendations, to determine a first performance score of the firstmodel and a second performance score of the second model and providefeedback for updating the first model and/or the second model. Toprocess the user-generated target recommendations, the firstrecommendations, and the second recommendations, the testing platform110 may determine first distance metrics associated with theuser-generated target recommendations and the first recommendations andsecond distance metrics associated with the user-generated targetrecommendations and the second recommendations. The first distancemetrics may include first distances between respective positions of theuser-generated target recommendations and respective positions of thefirst recommendations. For example, with respect to a recommendation ofthe user-generated target recommendations and/or the firstrecommendations, the testing platform 110 may calculate a first distancebetween a position of the recommendation in the user-generated targetrecommendations and a position of the recommendation in the firstrecommendations. The second distance metrics may include seconddistances between the respective positions of the user-generated targetrecommendations and respective positions of the second recommendations.For example, with respect to a recommendation of the user-generatedtarget recommendations and/or the second recommendations, the testingplatform 110 may calculate a second distance between a position of therecommendation in the user-generated target recommendations and aposition of the recommendation in the second recommendations.

The testing platform 110 may calculate, based on the first distancemetrics, a first score and/or a first weighted score for the firstrecommendations. To calculate the first score, the testing platform 110may combine the first distances. In some implementations, the testingplatform 110 may assign respective weights to the user-generated targetrecommendations (e.g., based on positions of the user-generated targetrecommendations). The testing platform 110 may multiply the respectiveweights by the respective first distances to define first weighteddistances. To calculate the first weighted score for the firstrecommendations, the testing platform 110 may combine the first weighteddistances.

Similarly, the testing platform 110 may calculate, based on the seconddistance metrics, a second score and/or a second weighted score for thesecond recommendations. To calculate the second score, the testingplatform 110 may combine the second distances. In some implementations,after assigning the respective weights to the respective positions ofthe user-generated target recommendations, the testing platform 110 maymultiply the respective weights by the respective second distances todefine second weighted distances. To calculate the second weighted scorefor the second recommendations, the testing platform 110 may combine thesecond weighted distances.

For example, when comparing the user-generated target recommendationsand the first recommendations, the testing platform 110 may determinethat “Men in Black II” is in a first position in the user-generatedtarget recommendations but is not provided in the first recommendations.To calculate a first distance of “Men in Black II” within theuser-generated target recommendations with respect to the firstrecommendations, the testing platform 110 may operate based on anassumption that “Men in Black II” would have occupied a fourth positionof the first recommendations. Thus, by subtracting 1 (e.g., of the firstposition of the user-recommended target recommendations) from 4 (e.g.,of the fourth position of the first recommendations), the testingplatform 110 may determine that the first distance of “Men in Black II”is 3. Looking at a second position in the user-generated targetrecommendations, “Independence Day” has a first distance of 1 because“Independence Day” is listed within a first position of the firstrecommendations. Applying this process to remaining movies within theuser-generated target recommendations and/or the first recommendations,“I am Legend” has a first distance of 3 (e.g., based on the samerationale as “Men in Black II,” as described above), “Alien” has a firstdistance of 2 (e.g., based on an assumption that “Alien” would haveoccupied a fourth position of the user-recommended targetrecommendations), and “Guardians of the Galaxy” has a first distance of1 (e.g., based on an assumption that “Guardians of the Galaxy” wouldhave occupied a fourth position of the user-generated targetrecommendations). By combining the first distances, the testing platform110 may determine that the first score of the first recommendations is10.

Similarly, in this example, when comparing the user-generated targetrecommendations and the second recommendations, the testing platform 110may determine that “Men in Black II” has a second distance of 0,“Independence Day” has a second distance of 1, and “I am Legend” has asecond distance of 1. As a result, the testing platform 110 maycalculate that the second score of the second model is 2. Because thesecond score is less than the first score, the testing platform 110 maydetermine that the second recommendations of the second model were moreclosely aligned with the user-generated target recommendations of theuser. Thus, the second recommendations, at least with respect tointerests of the user, may have been more accurate than the firstrecommendations.

As another example, based on the first distances and the seconddistances, the testing platform 110 may calculate and compare the firstweighted score and the second weighted score. To calculate the firstweighted score and the second weighted score, the testing platform 110may select and assign a first weight of 1 to “Men in Black II,” whichoccupies the first position of the user-generated targetrecommendations, a second weight of 1.1 to “Independence Day,” whichoccupies the second position, and a third weight of 1.2 to “I amLegend,” which occupies the third position. Thus, the first weighteddistances may be, respectively, 3, 1.1, and 3.6. Combining the firstweighted distances, the testing platform 110 may calculate that thefirst weighted score is 7.6. Applying this process to calculate thesecond weighted score, the testing platform 110 may determine that thesecond weighted distances are, respectively, 0, 1.1, and 1.2. Combiningthe second weighted distances, the testing platform 110 may calculatethat the second weighted score is 2.3. Because the second weighted scoreis less than the first weighted score, the testing platform 110 maydetermine that the second recommendations of the second model were moreclosely aligned with the user-generated target recommendations of theuser and therefore more accurate than the first recommendations.

As the testing platform 110 obtains and processes user-generatedrecommendations from other users associated with the client devices 115,the testing platform 110 may combine and/or average, respectively, firstscores, first weighted scores, second scores, and/or second weightedscores to define a first combined/average score, a firstcombined/average weighted score, a second combined/average score, asecond combined/average weighted score, and/or the like. The testingplatform 110 may determine the first performance score based on thefirst combined/average score and/or the first combined/average weightedscore. Likewise, the testing platform 110 may determine the secondperformance score based on the second combined/average score and/or thesecond combined/average weighted score.

The testing platform 110 may store the first scores, the first weightedscores, the first combined/average score, the first combined/averageweighted score, the first performance score, the second scores, thesecond weighted scores, the second combined/average score, the secondcombined/average weighted score, and/or the second performance score ina data structure to assess accuracy and/or performance of the firstmodel and the second model. For example, based on the first performancescore and/or the second performance score, the testing platform 110 maydetermine whether the first model and/or the second model should beimplemented within a content delivery system, whether the first modeland/or the second model should be modified and/or deleted, whether othermodels should be trained and/or evaluated, and/or the like. Based on theuser-generated target recommendations, the testing platform 110 maygenerate feedback to provide to the first model generator and/or thesecond model generator for respectively updating the first model and/orthe second model.

It should be understood that the process described in connection withFIGS. 1C-1E is provided merely as example. In practice, the process maybe iterative and may involve users evaluating multiple content items andcorresponding recommendations, different types of content items andcorresponding recommendations, different methods of calculating thefirst score, the second score, the first weighted score, the secondweighted score, the first combined/average score, the secondcombined/average score, the first combined/average weighted score, thesecond combined/average weighted score, the first performance score,and/or the second performance score, and/or the like.

Turning to FIG. 1F, assume, for example, that the testing platform 110or the client devices 115 provide the selection of the second testingmode after selecting the first testing mode. It should be understood,however, that the sequence of selecting the second testing mode afterselecting the first testing mode is provided merely as an example. Inpractice, the testing platform 110 or the client devices 115 may providethe selection of the second testing mode instead of selecting the firsttesting mode, prior to selecting the first testing mode, and/or thelike.

Upon selection of the second testing mode, the testing platform 110 mayselect (e.g., randomly, based on a particular order, and/or the like) asecond content data set from the content data. The second content dataset may include a second identifier of a second content item, a secondset of metadata, and/or the like. If the first model and the secondmodel have been trained based on different types of metadata, the secondset of metadata may include a third subset of metadata for input intothe first model and a fourth subset of metadata for input into thesecond model. It should be understood that the method of selecting thesecond testing mode and/or the second content data set is providedmerely as an example. In practice, similar to that described above withrespect to FIGS. 1C-1E, the testing platform 110 may select the secondtesting mode, the users associated with the client devices 115 mayselect the second identifier, and/or the like.

As shown by reference number 150 in FIG. 1F, the testing platform 110may process, within the second testing mode, the second set of metadataassociated with the second content item, to generate thirdrecommendations from the first model and fourth recommendations from thesecond model. For example, the testing platform 110 may input the secondset of metadata associated with the second content item (or,respectively, the third subset of metadata and the fourth subset ofmetadata) into the first model and the second model. After the firstmodel and the second model process the second set of metadata associatedwith the second content item (or, respectively, the third subset ofmetadata and the fourth subset of metadata), the first model maygenerate the third recommendations, and the second model may generatethe fourth recommendations.

For example, assume that the second content item is a second movie, suchas “Pride and Prejudice.” The testing platform 110, similar to thatdescribed above with respect to the first movie, may input the secondset of metadata or the third subset of metadata into the first model.Based on the second set of metadata or the third subset of metadata, thefirst model may determine scores of recommended content items of thecontent data. Based on the scores, the first model may generate thethird recommendations (e.g., “Emma,” “Atonement,” “Love Actually,”and/or the like). In some implementations, the testing platform 110 mayarrange the third recommendations in a third order (e.g., from highestto lowest score, and/or the like). For example, the third order of thethird recommendations may be (1) “Emma,” (2) “Atonement,” and (3) “LoveActually.” Based on input of the second set of metadata or the fourthsubset of metadata, the second model may likewise determine scores ofrecommended content items and generate the fourth recommendations (e.g.,“The Notebook,” “Anna Karenina,” “Emma,” and/or the like). In someimplementations, the testing platform 110 may arrange the fourthrecommendations in a fourth order (e.g., from highest to lowest score,and/or the like). For example, the fourth order of the fourthrecommendations may be (1) “The Notebook”, (2) “Anna Karenina,” and (3)“Emma.” The testing platform 110 may store the third recommendations andthe fourth recommendations for later assessment.

As shown by reference number 155 in FIG. 1G, the testing platform 110may provide the second identifier of the second content item, the thirdrecommendations, and the fourth recommendations to the client devices115. For example, the user interface may display information regardingthe second movie, “Pride and Prejudice,” along with the thirdrecommendations in a first grouping and the fourth recommendations in asecond grouping. The user interface may prompt the users to select agrouping of the first grouping or the second grouping that the usersdetermine to be more accurate as a whole. In response, the users mayselect the first grouping or the second grouping. Thus, as shown byreference number 160, the client devices 115 may provide one or morefirst inputs indicating selection of the third recommendations and/orone or more second inputs indicating selection of the fourthrecommendations to the testing platform 110.

As shown by reference number 165 in FIG. 1H, the testing platform 110may process, within the second testing mode, the one or more firstinputs and/or the one or more second inputs, to update (or determine)the first performance score of the first model and the secondperformance score of the second model and provide feedback for updatingthe first model and/or the second model. To process the one or morefirst inputs and/or the one or more second inputs, the testing platform110 may calculate a first conversion value for the first model based ona number of the one or more first inputs and calculate a secondconversion value for the second model based on a number of the one ormore second inputs. The first conversion value may be the number of theone or more first inputs divided by a total of the one or more firstinputs and the one or more second inputs. The second conversion valuemay be the number of the one or more second inputs divided by the totalof the one or more first inputs and the one or more second inputs.

For example, when processing the one or more first inputs and the one ormore second inputs, the testing platform 110 may determine that thenumber of one or more first inputs is 40 and the number of one or moresecond inputs is 60. Thus, the testing platform 110 may calculate thatthe first conversion value is 40% (i.e., 40/(40+60)=0.4 or 40%) and thesecond conversion value is 60% (i.e., 60/(40+60)=0.6 or 60%). Becausethe second conversion value is greater than the first conversion value,the testing platform 110 may determine that an aggregate performance ofthe second model is better than an aggregate performance of the firstmodel.

After the testing platform 110 obtains the one or more first inputsand/or the one or more second inputs and calculates the first conversionvalue and/or the second conversion value, the testing platform 110 mayupdate the first performance score and/or the second performance score(e.g., by combining the first conversion value and the previouslydetermined first performance score, by combining the second conversionvalue and the previously determined second performance score, and/or thelike). In some implementations, for example if the users associated withthe client devices 115 or the testing platform 110 selects the secondtesting mode instead of the first testing mode, the testing platform 110may determine the first performance score and the second performancescore based on the first conversion value and the second conversionvalue, respectively. Along with the values described above inassociation with the first testing mode, the testing platform 110 maystore the number of the one or more first inputs, the first conversionvalue, the number of the one or more second inputs, and/or the secondconversion value to assess accuracy and/or performance of the firstmodel and the second model.

As described above with respect to the first testing mode, the testingplatform 110 may determine, based on the first performance score and/orthe second performance score, whether the first model and/or the secondmodel should be implemented within a content delivery system, whetherthe first model and/or the second model should be modified and/ordeleted, whether other models should be trained and/or evaluated, and/orthe like. Based on content of the one or more second inputs and/or theone or more first inputs, respectively, the testing platform 110 maygenerate feedback to provide to the first model generator and/or thesecond model generator for respectively updating the first model and/orthe second model.

It should be understood that the process described in connection withFIGS. 1F-1H is provided merely as example. In practice, the process maybe iterative and may involve users evaluating multiple content items andcorresponding recommendations, different types of content items andcorresponding recommendations, different methods of calculating thefirst conversion value, the second conversion value, the firstperformance score, and/or the second performance score, and/or the like.

Turning to FIG. 1I, assume, for example, that the testing platform 110or the client devices 115 provide the selection of the third testingmode after selecting the second testing mode. It should be understood,however, that the sequence of selecting the third testing mode afterselecting the second testing mode is provided merely as an example. Inpractice, the testing platform 110 or the client devices 115 may providethe selection of the third testing mode instead of selecting the firsttesting mode and/or the second testing mode, prior to selecting thefirst testing mode and/or the second testing mode, and/or the like.

Upon selection of the third testing mode, the testing platform 110 mayselect (e.g., randomly, based on a particular order, and/or the like) athird content data set from the content data. The third content data setmay include a third identifier of a third content item, a third set ofmetadata, and/or the like. If the first model and the second model havebeen trained based on different types of metadata, the third set ofmetadata may include a fifth subset of metadata for input into the firstmodel and a sixth subset of metadata for input into the second model. Itshould be understood that the method of selecting the third testing modeand/or the second content data set is provided merely as an example. Inpractice, similar to that described above, the testing platform 110 mayselect the third testing mode, the users associated with the clientdevices 115 may select the third identifier, and/or the like.

As shown by reference number 170 in FIG. 1I, the testing platform 110may process, within the third testing mode, the third set of metadataassociated with the third content item, to generate a fifthrecommendation from the first model and a sixth recommendation from thesecond model. For example, the testing platform 110 may input the thirdset of metadata associated with the third content item (or,alternatively, the fifth subset of metadata and the sixth subset ofmetadata) into the first model and the second model. After the firstmodel and the second model process the third set of metadata associatedwith the third content item (or, respectively, the fifth subset ofmetadata and the sixth subset of metadata), the first model may generatethe fifth recommendation, and the second model may generate the sixthrecommendation.

For example, assume that the third content item is a third movie, suchas “Ferris Bueller's Day Off.” The testing platform 110, similar to thatdescribed above with respect to the first movie and/or the second movie,may input the third set of metadata or the fifth subset of metadata intothe first model. Based on the third set of metadata or the fifth subsetof metadata, the first model may determine scores of recommended contentitems of the content data. The testing platform 110 may select the fifthrecommendation (e.g., “Breakfast Club” and/or the like) as therecommended content item with a highest score. Based on the third set ofmetadata or the sixth subset of metadata, the second model may likewisedetermine scores of recommended content items. The testing platform 110may select the sixth recommendation (e.g., “Planes, Trains, andAutomobiles” and/or the like) as the recommended content item with ahighest score. The testing platform 110 may store the fifthrecommendation and the sixth recommendation for later assessment.

As shown by reference number 175 in FIG. 1J, the testing platform 110may provide, via a user interface, the third identifier of the thirdcontent item, the fifth recommendation, and the sixth recommendation tothe client devices 115. For example, the user interface may displayinformation regarding the second movie, “Ferris Bueller's Day Off,”along with the fifth recommendation, “Breakfast Club,” and the sixthrecommendation, “Planes, Trains, and Automobiles.” The user interfacemay prompt the users associated with the client devices 115 to select arecommendation of the fifth recommendation or the sixth recommendationthat the users determine to be more accurate. In response, the users mayselect the fifth recommendation or the sixth recommendation. Thus, asshown by reference number 180, the client devices 115 may provide one ormore third inputs indicating selection of the fifth recommendationand/or one or more fourth inputs indicating selection of the sixthrecommendation to the testing platform 110.

As shown by reference number 185 in FIG. 1K, the testing platform 110may process, within the third testing mode, the one or more third inputsand/or the one more fourth inputs, to update (or determine) the firstperformance score of the first model and the second performance score ofthe second model and provide feedback for updating the first modeland/or the second model. To process the one or more third inputs and/orthe one or more fourth inputs, the testing platform 110 may calculate athird conversion value for the first model based on a number of the oneor more third inputs and calculate a fourth conversion value for thesecond model based on a number of the one or more fourth inputs. Thethird conversion value may be the number of the one or more third inputsdivided by a total of the one or more third inputs and the one or morefourth inputs. The fourth conversion value may be the number of the oneor more fourth inputs divided by the total of the one or more thirdinputs and the one or more fourth inputs.

For example, when processing the one or more third inputs and the one ormore fourth inputs, the testing platform 110 may determine that thenumber of one or more third inputs is 75 and the number of one or morefourth inputs is 25. Thus, the testing platform 110 may calculate thatthe third conversion value is 75% (i.e., 75/(75+25)=0.75 or 75%) and thefourth conversion value is 25% (i.e., 25/(75+25)=0.25 or 25%). Becausethe third conversion value is greater than the fourth conversion value,the testing platform 110 may determine that the fifth recommendation ismore accurate than the sixth recommendation.

After the testing platform 110 obtains the one or more third inputsand/or the one or more fourth inputs and calculates the third conversionvalue and/or the fourth conversion value, the testing platform 110 mayupdate the first performance score and/or the second performance scorebased on the third conversion value, the fourth conversion value, and/orthe like. In some implementations, for example if the users associatedwith the client devices 115 or the testing platform 110 select the thirdtesting mode instead of the first testing mode and the second testingmode, the testing platform 110 may determine the first performance scoreand the second performance score based on the third conversion value andthe fourth conversion value, respectively. Along with the valuesdescribed above in association with the first testing mode and thesecond testing mode, the testing platform 110 may store the number ofthe one or more third inputs, the third conversion value, the number ofthe one or more fourth inputs, and/or the fourth conversion value toassess accuracy and/or performance of the first model and the secondmodel.

As described above with respect to the first testing mode and the secondtesting mode, the testing platform 110 may determine, based on the firstperformance score and/or the second performance score, whether the firstmodel and/or the second model should be implemented within a contentdelivery system, whether the first model and/or the second model shouldbe modified and/or deleted, whether other models should be trainedand/or evaluated, and/or the like. Based on content of the one or morefourth inputs and/or the one or more third inputs, respectively, thetesting platform 110 may generate feedback to provide to the first modelgenerator and/or the second model generator for respectively updatingthe first model and/or the second model.

It should be understood that the process described in connection withFIGS. 1I-1K is provided merely as example. In practice, the process maybe iterative and may involve users evaluating multiple content items andcorresponding recommendations, different types of content items andcorresponding recommendations, different methods of calculating thethird conversion value, the fourth conversion value, the firstperformance score, and/or the second performance score, and/or the like.

As shown by reference number 190 in FIG. 1L, the testing platform 110may perform one or more actions based on the first performance score,the second performance score, and/or the feedback. The one or moreactions may include generating a report, causing the first model and/orthe second model to be updated and reevaluated, causing the first modeland/or the second model to be deleted, and/or the like. For example,based on the first performance score and/or the second performancescore, the testing platform 110 may generate a report to summarizeresults of the model evaluation process. The report may indicate thecontent items processed by the first model and/or the second model,recommendations made by the first model and/or the second model, thefirst combined/average score, the first combined/average weighted score,the second combined/average score, the second combined/average weightedscore, the number of one or more first inputs, the first conversionvalue, the number of one or more second inputs, the second conversionvalue, the number of one or more third inputs, the third conversionvalue, the number of one or more fourth inputs, the fourth conversionvalue, first performance score, the second performance score, and/or thelike. The testing platform 110 may present the report for display on auser interface, transmit the report via email, and/or the like.

Additionally, or alternatively, the testing platform 110 may cause thefirst model and/or the second model to be updated, based on the firstperformance score, the second performance score, and/or the feedback.For example, the testing platform 110 may provide the feedback to thefirst model generator to update the first model (e.g., based on theuser-generated content recommendations, content of the one or moresecond inputs, content of the one or more fourth inputs, and/or thelike). After the first model generator updates the first model andprovides the first model to the testing platform 110, the testingplatform 110 may reevaluate the first model (e.g., within the firsttesting mode, the second testing mode, the third testing mode, and/orthe like). Likewise, the testing platform 110 may provide the feedbackto the second model generator to update the second model (e.g., based onthe user-generated target recommendations, content of the one or morefirst inputs, content of the one or more third inputs, and/or the like).After the second model generator updates the second model and providesthe second model to the testing platform 110, the testing platform 110may reevaluate the second model (e.g., within the first testing mode,the second testing mode, the third testing mode, and/or the like). Thus,the testing platform 110 may determine, via the model evaluationprocess, which models have potential and cause those models to beimproved and reevaluated.

Additionally, or alternatively, the testing platform 110 may cause thefirst model and/or the second model to be deleted, based on the firstperformance score and/or the second performance score. For example,based on the first performance score failing to satisfy a performancethreshold after a set number of iterations of evaluation, failing toimprove by a threshold percentage, and/or the like, the testing platform110 may instruct the first model generator to delete the first model.Likewise, based on the second performance score failing to satisfy aperformance threshold after a set number of iterations, failing toimprove by a threshold percentage, the testing platform 110 may instructthe second model generator to delete the second model. Thus, the testingplatform 110 may determine, via the model evaluation process, whichmodels lack potential and cause those models to be deleted.

Over time, the testing platform 110 may determine that one of the firstperformance score, the second performance score, and/or anotherperformance score of another model separates above remaining performancescores of the first performance score, the second performance score,and/or the other performance score. For example, after the set number ofiterations of evaluation, after a set period of time, and/or the like,the testing platform 110 may determine that the first performance scoreor the second performance score is a higher performance score by someamount than the other performance scores and is thus indicative of thebest model. As a result, as shown by reference number 195 in FIG. 1M,the testing platform 110 may provide, based on the first performancescore or the second performance score, the first model or the secondmodel to the server device 105 to cause the server device 105 toimplement the first model or the second model within the contentdelivery system.

Because the testing platform 110 enables models to be evaluated relativeto a plurality of content data sets, over a plurality of client devices,and in a plurality of testing modes, the testing platform 110 mayfacilitate data analysis and, as a result, improve the models. Byutilizing an iterative process of evaluation, the testing platform 110may identify the best model for implementation within the contentdelivery system. Furthermore, the testing platform 110 may conservecomputing resources and/or network resources that might otherwise havebeen consumed storing large amounts of data relating to contentselection, analyzing the large amounts of data, and/or the like.

As indicated above, FIGS. 1A-1M are provided as an example. Otherexamples may differ from what is described with regard to FIGS. 1A-1M.The number and arrangement of devices shown in FIGS. 1A-1M are providedas an example. In practice, there may be additional devices, fewerdevices, different devices, or differently arranged devices than thoseshown in FIGS. 1A-1M. Furthermore, two or more devices shown in FIGS.1A-1M may be implemented within a single device, or a single deviceshown in FIGS. 1A-1M may be implemented as multiple, distributeddevices. Additionally, or alternatively, a set of devices (e.g., one ormore devices) shown in FIGS. 1A-1M may perform one or more functionsdescribed as being performed by another set of devices shown in FIGS.1A-1M.

FIG. 2 is a diagram illustrating an example 200 of training and using amodel (e.g., the first model, the second model, and/or the like) togenerate recommendations. The model training and usage described hereinmay be performed using a machine learning system. The machine learningsystem may include or may be included in a computing device, a server, acloud computing environment, and/or the like, such as the first modelgenerator, the second model generator, and/or the testing platform 110,which are described in more detail elsewhere herein.

As shown by reference number 205, a model may be trained using a set ofobservations. The set of observations may be obtained from historicaldata, such as data gathered during one or more processes describedherein. The historical data may include historical data relating tocontent items (e.g., one or more types of metadata of the content items,and/or the like), historical data relating to selections made based onthe content items (e.g., selection history of the content items relativeto other content items, and/or the like), historical data relating touser feedback (e.g., user feedback reflective of user interests, and/orthe like). In some implementations, the machine learning system mayreceive the set of observations (e.g., as input) from a server device(e.g., server device 105) and/or client devices (e.g., the clientdevices 115), as described elsewhere herein.

As shown by reference number 210, the set of observations includes afeature set (e.g., a set of metadata, and/or the like). The feature setmay include a set of variables, and a variable may be referred to as afeature. A specific observation may include a set of variable values (orfeature values) corresponding to the set of variables. In someimplementations, the machine learning system may determine variables fora set of observations and/or variable values for a specific observationbased on input received from client devices. For example, the machinelearning system may identify a feature set (e.g., one or more featuresand/or feature values) by extracting the feature set from structureddata, by performing natural language processing to extract the featureset from unstructured data, by receiving input from an operator, and/orthe like.

As an example, a feature set for a set of observations may include afirst feature of “Genre,” a second feature of “Actor,” a third featureof “Release Year,” and so on. As shown, for a first observation (e.g., acontent item, such as the movie “Sound of Music”), the first feature maybe “Musical,” the second feature may be “Julie Andrews,” the thirdfeature may be “1965,” and so on. These features and feature values areprovided as examples, and may differ in other examples. For example, thefeature set may include, with respect to an observation, one or more ofthe following features: a rating, an award, a director, an artist, acreator, an author, a publisher, a movie studio, a television network, arecord label, a type, a language, and/or the like.

As shown by reference number 215, the set of observations may beassociated with a target variable. The target variable may represent avariable having a numeric value, may represent a variable having anumeric value that falls within a range of values or has some discretepossible values, may represent a variable that is selectable from one ofmultiple options (e.g., one of multiples classes, classifications,labels, and/or the like), may represent a variable having a Booleanvalue, and/or the like. A target variable may be associated with atarget variable value, and a target variable value may be specific to anobservation. In example 200, the target variable is “Recommendation,”which may be “The King and I” for the first observation.

The feature set and target variable described above are provided asexamples, and other examples may differ from what is described above.For example, for a target variable of “Goodfellas,” the feature set mayinclude “Crime Fiction,” “Marlon Brando,” “1972,” and/or the like.

The target variable may represent a value that a model is being trainedto predict, and the feature set may represent the variables that areinput to a trained model to predict a value for the target variable. Theset of observations may include target variable values so that the modelcan be trained to recognize patterns in the feature set that lead to atarget variable value. A model that is trained to predict a targetvariable value may be referred to as a supervised learning model.

In some implementations, the model may be trained on a set ofobservations that do not include a target variable. This may be referredto as an unsupervised learning model. In this case, the model may learnpatterns from the set of observations without labeling or supervision,and may provide output that indicates such patterns, such as by usingclustering and/or association to identify related groups of items withinthe set of observations.

As shown by reference number 220, the machine learning system may traina model using the set of observations and using one or more machinelearning algorithms, such as a regression algorithm, a decision treealgorithm, a neural network algorithm, a k-nearest neighbor algorithm, asupport vector machine algorithm, and/or the like. After training, themachine learning system may store the model as a trained model 225 to beused to analyze new observations.

As shown by reference number 230, the machine learning system may applythe trained model 225 to a new observation, such as by receiving a newobservation and inputting the new observation to the trained model 225.As shown, the new observation may include a first feature of “Family,” asecond feature of “Tom Hanks,” a third feature of “1995,” and so on, asan example. The machine learning system may apply the trained model 225to the new observation to generate an output (e.g., a result). The typeof output may depend on the type of model and/or the type of machinelearning task being performed. For example, the output may include apredicted value of a target variable, such as when supervised learningis employed. Additionally, or alternatively, the output may includeinformation that identifies a cluster to which the new observationbelongs, information that indicates a degree of similarity between thenew observation and one or more other observations, and/or the like,such as when unsupervised learning is employed.

As an example, the trained model 225 may predict a recommendation of“Toy Story 2” for the new observation, as shown by reference number 235.Based on this prediction, the machine learning system may provide afirst recommendation, may provide output for determination of a firstrecommendation, may perform a first automated action, may cause a firstautomated action to be performed (e.g., by instructing another device toperform the automated action), and/or the like.

In some implementations, the trained model 225 may classify (e.g.,cluster) the new observation in a cluster, as shown by reference number240. The observations within a cluster may have a threshold degree ofsimilarity. As an example, if the machine learning system classifies thenew observation in a first cluster, then the machine learning system mayprovide a first recommendation, such as the first recommendationdescribed above. Additionally, or alternatively, the machine learningsystem may perform a first automated action and/or may cause a firstautomated action to be performed (e.g., by instructing another device toperform the automated action) based on classifying the new observationin the first cluster, such as the first automated action describedabove.

In some implementations, the recommendation and/or the automated actionassociated with the new observation may be based on a target variablevalue having a particular label (e.g., classification, categorization,and/or the like), may be based on whether a target variable valuesatisfies one or more threshold (e.g., whether the target variable valueis greater than a threshold, is less than a threshold, is equal to athreshold, falls within a range of threshold values, and/or the like),may be based on a cluster in which the new observation is classified,and/or the like.

In this way, the machine learning system may apply a rigorous andautomated process to generate content recommendations based on a contentitem. The machine learning system enables recognition and/oridentification of tens, hundreds, thousands, or millions of featuresand/or feature values for tens, hundreds, thousands, or millions ofobservations, thereby increasing accuracy and consistency and reducingdelay associated with generating recommendations relative to requiringcomputing resources to be allocated for tens, hundreds, or thousands ofoperators to manually generate recommendations using the features orfeature values.

As indicated above, FIG. 2 is provided as an example. Other examples maydiffer from what is described in connection with FIG. 2. The feature setand target variables are provided as example. In practice, there may beadditional features, fewer features, or different features than thoseshown in FIG. 2.

FIG. 3 is a diagram of an example environment 300 in which systemsand/or methods described herein may be implemented. As shown in FIG. 3,environment 300 may include a server device 105, a testing platform 110,a client device 115 (e.g., of the client devices 115), and/or a network320. Devices of environment 300 may interconnect via wired connections,wireless connections, or a combination of wired and wirelessconnections.

The server device 105 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information, such asinformation described herein. For example, the server device 105 mayinclude a laptop computer, a tablet computer, a desktop computer, agroup of server devices, or a similar device. The server device 105 mayreceive information from and/or transmit information to the testingplatform 110.

The testing platform 110 includes one or more devices capable ofreceiving, generating, storing, processing, and/or providing informationassociated with evaluating models that generate recommendations. Thetesting platform 110 may include one or more elements of a cloudcomputing system 302 and/or may execute within the cloud computingsystem 302 (e.g., as one or more virtual computing systems 306). Thecloud computing system 302 may include one or more elements 303-317, asdescribed in more detail below.

The cloud computing system 302 includes computing hardware 303, aresource management component 304, a host operating system (OS) 305,and/or one or more virtual computing systems 306. The resourcemanagement component 304 may perform virtualization (e.g., abstraction)of computing hardware 303 to create the one or more virtual computingsystems 306. Using such virtualization, the resource managementcomponent 304 enables a single computing device (e.g., a computer, aserver, a host device, and/or the like) to operate as if the singlecomputing device were multiple computing devices, such as by creatingmultiple isolated virtual computing systems 306 from computing hardware303 of the single computing device. The multiple virtual computingsystems 306 operate independently from one another and do not interactwith one another. In this way, computing hardware 303 can operate moreefficiently, with lower power consumption, higher reliability, higheravailability, higher utilization, greater flexibility, and lower costthan using separate computing devices.

Computing hardware 303 includes hardware and corresponding resourcesfrom one or more computing devices. For example, computing hardware 303may include hardware from a single computing device (e.g., a singleserver or host device) or from multiple computing devices (e.g.,multiple servers or host devices), such as multiple computing devices inone or more data centers, server farms, server pools, and/or the like.As shown, computing hardware 303 may include one or more processors 307,one or more memories 308, one or more storage components 309, and/or oneor more networking components 310. Computing hardware 303 may beinterconnected via one or more wired and/or wireless buses, which mayinterconnect computing hardware 303 within a single computing deviceand/or across multiple computing devices.

A processor 307 includes a central processing unit, a graphicsprocessing unit, and/or the like. A memory 308 includes random accessmemory, read-only memory, and/or the like. The memory 308 may store aset of instructions (e.g., one or more instructions) for execution bythe processor 307. The processor 307 may execute the set of instructionsto perform one or more operations or processes described herein. In someimplementations, execution of the set of instructions, by one or moreprocessors 307, causes the one or more processors 307 and/or the testingplatform 110 to perform one or more operations or processes describedherein. A storage component 309 includes a hard disk or another type ofstorage device that stores information, data, and/or software (e.g.,code, instructions, and/or the like) related to the operation and use ofthe testing platform 110. In some implementations, memory 308 and/orstorage component 309 is/are implemented as a non-transitory computerreadable medium. A networking component 310 includes a network interfaceand corresponding hardware that enables the testing platform 110 tocommunicate with other devices of environment 300 via a wired connectionand/or a wireless connection, such as via network 320. Additionalexamples of a processor, a memory, a storage component, and a networkingcomponent (e.g., a communication interface) are described elsewhereherein.

The resource management component 304 includes a virtualizationapplication (e.g., executing on hardware, such as computing hardware303) capable of virtualizing computing hardware 303 to start (e.g.,create or spin up), stop (e.g., delete or tear down), and/or manage oneor more virtual computing systems 306. Such virtualization may includeoperating system virtualization, shared kernel virtualization (e.g.,container-based virtualization), kernel level virtualization, hypervisorvirtualization, paravirtualization, full virtualization, hardwarevirtualization, and/or the like. The resource management component 304may control access to and/or use of computing hardware 303 and/orsoftware executing on computing hardware 303. Additionally, oralternatively, the resource management component 304 may perform binaryrewriting to scan instructions received from a virtual computing system306 and replace any privileged instructions with safe emulations ofthose instructions. The resource management component 304 may include ahypervisor or a virtual machine monitor, such as when the virtualcomputing systems 306 are virtual machines 311. Additionally, oralternatively, the resource management component 304 may include acontainer manager, such as when the virtual computing systems 306 arecontainers 312.

In some implementations, the resource management component 304 executeswithin and/or in coordination with a host operating system 305. Forexample, the resource management component 304 may execute on top of thehost operating system 305 rather than interacting directly withcomputing hardware 303, such as when the resource management component304 is a hosted hypervisor (e.g., a Type 2 hypervisor) or a containermanager. In this case, the host operating system 305 may control accessto and/or use of computing hardware 303 and/or software executing oncomputing hardware 303 based on information and/or instructions receivedfrom the resource management component 304. Alternatively, the resourcemanagement component 304 may interact directly with computing hardware303 rather than interacting with the host operating system 305, such aswhen the resource management component 304 is a bare-metal hypervisor(e.g., a Type 1 hypervisor). Thus, in some implementations, the cloudcomputing system 302 does not include a host operating system 305. Insome implementations, the host operating system 305 includes and/orexecutes an administrator application to enable a system administratorto manage, customize, and/or configure cloud computing system 302.

A virtual computing system 306 includes a virtual environment thatenables cloud-based execution of operations and/or processes describedherein using computing hardware 303. As shown, a virtual computingsystem 306 may include a virtual machine 311, a container 312, a hybridenvironment 313 that includes a virtual machine and a container, and/orthe like. A virtual computing system 306 may execute one or moreapplications 314 using a file system 315. The file system 315 mayinclude binary files, software libraries, and/or other resourcesrequired to execute applications 314 on a guest operating system 316 orthe host operating system 305. In some implementations, a virtualcomputing system 306 (e.g., a virtual machine 311 or a hybridenvironment 313) includes a guest operating system 316. In someimplementations, a virtual computing system 306 (e.g., a container 312or a hybrid environment 313) includes a container manager 317.

A virtual machine 311 is an emulation of a computing device that enablesexecution of separate, isolated instances of virtual computing devices(e.g., multiple virtual machines 311) on the same computing hardware303. The guest operating systems 316 and applications 314 of multiplevirtual machines 311 may share computing hardware 303 from a singlecomputing device or from multiple computing devices (e.g., a pool ofcomputing devices). Each separate virtual machine 311 may include aguest operating system 316, a file system 315, and one or moreapplications 314. With a virtual machine 311, the underlying computinghardware 303 is virtualized, and the guest operating system 316 executeson top of this virtualized hardware. Using virtual machines 311 enablesdifferent types of guest operating systems 316 to execute on the samecomputing hardware 303 in an isolated environment, but with moreresource usage and overhead than containers 312.

Unlike a virtual machine 311, a container 312 virtualizes a hostoperating system 305 rather than the underlying computing hardware 303.Thus, a container 312 does not require a guest operating system 316because the application(s) 314 included in the container 312 executedirectly on the host operating system 305 using a file system 315included in the container 312. Each separate container 312 may share thekernel of the host operating system 305, and different applications 314within a single container 312 may share a file system 315. This sharingof a file system 315 among multiple applications 314 reduces the need toreproduce operating system code for different applications, and enablesa single host operating system 305 to execute multiple applications 314and/or containers 312. As a result, containers 312 enable a greaterquantity of applications 314 to execute on a smaller quantity ofcomputing devices as compared to virtual machines 311.

A hybrid environment 313 includes elements of a virtual machine 311 anda container 312. For example, a hybrid environment 313 may include aguest operating system 316 that executes on top of virtualized hardware.A container manager 317 may execute on top of the guest operating system316 to start, stop, and/or manage one or more containers within thehybrid environment 313. Using a hybrid environment 313 enables differenttypes of guest operating systems 316 to execute on the same computinghardware 303 in an isolated environment, while also enabling lightweightcontainers to execute on top of the guest operating system 316.

The quantity of applications 314 shown in FIG. 3 as executing withineach virtual computing system 306 is shown as an example, and adifferent quantity of applications 314 may execute within each virtualcomputing system. Furthermore, although the testing platform 110 mayinclude one or more elements 303-317 of the cloud computing system 302,may execute within the cloud computing system 302, and/or may be hostedwithin the cloud computing system 302, in some implementations, thetesting platform 110 may not be cloud-based (e.g., may be implementedoutside of a cloud computing system) or may be partially cloud-based.For example, the testing platform 110 may include one or more devicesthat are not part of the cloud computing system 302. The testingplatform 110 may perform one or more operations and/or processesdescribed in more detail elsewhere herein.

The client device 115 includes one or more devices capable of receiving,generating, storing, processing, and/or providing information, such asinformation described herein. For example, the client device 115 mayinclude a computing device (e.g., a desktop computer, a laptop computer,a tablet computer, a handheld computer, a server, etc.), a mobile phone(e.g., a smart phone, a radiotelephone, etc.), or a similar device. Insome implementations, the client device 115 may receive information fromand/or transmit information to the testing platform 110.

Network 320 includes one or more wired and/or wireless networks. Forexample, network 320 may include a cellular network (e.g., a fifthgeneration (5G) network, a fourth generation (4G) network, a long-termevolution (LTE) network, a third generation (3G) network, a codedivision multiple access (CDMA) network, etc.), a public land mobilenetwork (PLMN), a local area network (LAN), a wide area network (WAN), ametropolitan area network (MAN), a telephone network (e.g., the PublicSwitched Telephone Network (PSTN)), a private network, an ad hocnetwork, an intranet, the Internet, a fiber optic-based network, and/orthe like, and/or a combination of these or other types of networks. Thenetwork 320 enables communication among the devices of environment 300.

The number and arrangement of devices and networks shown in FIG. 3 areprovided as an example. In practice, there may be additional devicesand/or networks, fewer devices and/or networks, different devices and/ornetworks, or differently arranged devices and/or networks than thoseshown in FIG. 3. Furthermore, two or more devices shown in FIG. 3 may beimplemented within a single device, or a single device shown in FIG. 3may be implemented as multiple, distributed devices. Additionally, oralternatively, a set of devices (e.g., one or more devices) ofenvironment 300 may perform one or more functions described as beingperformed by another set of devices of environment 300.

FIG. 4 is a diagram of example components of a device 400. Device 400may correspond to the server device 105, the testing platform 110,and/or the client device 115. In some implementations, the server device105, the testing platform 110, and/or the client device 115 may includeone or more devices 400 and/or one or more components of device 400. Asshown in FIG. 4, device 400 may include a bus 410, a processor 420, amemory 430, a storage component 440, an input component 450, an outputcomponent 460, and a communication interface 470.

Bus 410 includes a component that permits communication among multiplecomponents of device 400. Processor 420 is implemented in hardware,firmware, and/or a combination of hardware and software. Processor 420is a central processing unit (CPU), a graphics processing unit (GPU), anaccelerated processing unit (APU), a microprocessor, a microcontroller,a digital signal processor (DSP), a field-programmable gate array(FPGA), an application-specific integrated circuit (ASIC), or anothertype of processing component. In some implementations, processor 420includes one or more processors capable of being programmed to perform afunction. Memory 430 includes a random access memory (RAM), a read onlymemory (ROM), and/or another type of dynamic or static storage device(e.g., a flash memory, a magnetic memory, and/or an optical memory) thatstores information and/or instructions for use by processor 420.

Storage component 440 stores information and/or software related to theoperation and use of device 400. For example, storage component 440 mayinclude a hard disk (e.g., a magnetic disk, an optical disk, and/or amagneto-optic disk), a solid state drive (SSD), a compact disc (CD), adigital versatile disc (DVD), a floppy disk, a cartridge, a magnetictape, and/or another type of non-transitory computer-readable medium,along with a corresponding drive.

Input component 450 includes a component that permits device 400 toreceive information, such as via user input (e.g., a touch screendisplay, a keyboard, a keypad, a mouse, a button, a switch, and/or amicrophone). Additionally, or alternatively, input component 450 mayinclude a component for determining location (e.g., a global positioningsystem (GPS) component) and/or a sensor (e.g., an accelerometer, agyroscope, an actuator, another type of positional or environmentalsensor, and/or the like). Output component 460 includes a component thatprovides output information from device 400 (via, e.g., a display, aspeaker, a haptic feedback component, an audio or visual indicator,and/or the like).

Communication interface 470 includes a transceiver-like component (e.g.,a transceiver, a separate receiver, a separate transmitter, and/or thelike) that enables device 400 to communicate with other devices, such asvia a wired connection, a wireless connection, or a combination of wiredand wireless connections. Communication interface 470 may permit device400 to receive information from another device and/or provideinformation to another device. For example, communication interface 470may include an Ethernet interface, an optical interface, a coaxialinterface, an infrared interface, a radio frequency (RF) interface, auniversal serial bus (USB) interface, a wireless local area networkinterface, a cellular network interface, and/or the like.

Device 400 may perform one or more processes described herein. Device400 may perform these processes based on processor 420 executingsoftware instructions stored by a non-transitory computer-readablemedium, such as memory 430 and/or storage component 440. As used herein,the term “computer-readable medium” refers to a non-transitory memorydevice. A memory device includes memory space within a single physicalstorage device or memory space spread across multiple physical storagedevices.

Software instructions may be read into memory 430 and/or storagecomponent 440 from another computer-readable medium or from anotherdevice via communication interface 470. When executed, softwareinstructions stored in memory 430 and/or storage component 440 may causeprocessor 420 to perform one or more processes described herein.Additionally, or alternatively, hardware circuitry may be used in placeof or in combination with software instructions to perform one or moreprocesses described herein. Thus, implementations described herein arenot limited to any specific combination of hardware circuitry andsoftware.

The number and arrangement of components shown in FIG. 4 are provided asan example. In practice, device 400 may include additional components,fewer components, different components, or differently arrangedcomponents than those shown in FIG. 4. Additionally, or alternatively, aset of components (e.g., one or more components) of device 400 mayperform one or more functions described as being performed by anotherset of components of device 400.

FIG. 5 is a flow chart of an example process 500 associated withevaluating models that generate recommendations. In someimplementations, one or more process blocks of FIG. 5 may be performedby a device (e.g., testing platform 110). In some implementations, oneor more process blocks of FIG. 5 may be performed by another device or agroup of devices separate from or including the device, such as serverdevice (e.g., server device 105), client devices (e.g., client devices115), and/or the like. Additionally, or alternatively, one or moreprocess blocks of FIG. 5 may be performed by one or more components ofdevice 400, such as processor 420, memory 430, storage component 440,input component 450, output component 460, communication interface 470,and/or the like.

As shown in FIG. 5, process 500 may include receiving content data, afirst model, and a second model (block 510). For example, the device mayreceive content data, a first model, and a second model, as describedabove. The content data may include a first identifier of a firstcontent item, a first set of metadata associated with the first contentitem, a second identifier of a second content item, a second set ofmetadata associated with the second content item, a third identifier ofa third content item, and/or a third set of metadata associated with thethird content item. The first model may be trained on different types ofdata than the second model. In some implementations, the first model maybe trained using a different technique than the second model.

As further shown in FIG. 5, process 500 may include processing a set ofmetadata associated with a content item, to generate firstrecommendations from the first model and second recommendations from thesecond model (block 520). For example, the device may process a firstset of metadata associated with a content item, to generate firstrecommendations from the first model and second recommendations from thesecond model, as described above. The set of metadata associated withthe content item may include the first set of metadata associated withthe first content item. Processing the set of metadata associated withthe content item may occur within a first testing mode.

As further shown in FIG. 5, process 500 may include providing anidentifier of the content item and a combination of the firstrecommendations and the second recommendations to client devices (block530). For example, the device may provide an identifier of the contentitem and a combination of the first recommendations and the secondrecommendations to client devices, as described above. The identifier ofthe content item may include the first identifier of the first contentitem.

As further shown in FIG. 5, process 500 may include receiving, from theclient devices, user-generated target recommendations based on thecombination of the first recommendations and the second recommendations(block 540). For example, the device may receive, from the clientdevices, user-generated target recommendations based on the combinationof the first recommendations and the second recommendations, asdescribed above. The user-generated target recommendations may includeat least one of the first recommendations or the second recommendations.

As further shown in FIG. 5, process 500 may include processing theuser-generated target recommendations, the first recommendations, andthe second recommendations, to provide feedback for updating the firstmodel or the second model (block 550). For example, the device mayprocess the user-generated target recommendations, the firstrecommendations, and the second recommendations, to provide feedback forupdating the first model or the second model, as described above.Processing the user-generated target recommendations, the firstrecommendations, and the second recommendations may occur within thefirst testing mode and may be to determine a first performance score ofthe first model and a second performance score of the second model.Processing the user-generated target recommendations, the firstrecommendations, and the second recommendations may comprise determiningfirst distance metrics associated with the user-generated targetrecommendations and the first recommendations; determining seconddistance metrics associated with the user-generated targetrecommendations and the second recommendations; calculating first scoresfor the first recommendations based on the first distance metrics;calculating second scores for the second recommendations based on thesecond distance metrics; calculating the first performance score of thefirst model based on the first scores to determine whether to cause thefirst model to be updated; and calculating the second performance scoreof the second model based on the second scores to determine whether tocause the second model to be updated. In some implementations,processing the user-generated target recommendations, the firstrecommendations, and the second recommendations may comprise determiningfirst distance metrics associated with the user-generated targetrecommendations and the first recommendations; determining seconddistance metrics associated with the user-generated targetrecommendations and the second recommendations; calculating firstweighted scores for the first recommendations based on the firstdistance metrics; calculating second weighted scores for the secondrecommendations based on the second distance metrics; calculating thefirst performance score based on the first weighted scores to determinewhether to cause the first model to be updated; and calculating thesecond performance score based on the second weighted scores todetermine whether to cause the second model to be updated. In someimplementations, process 500 may include generating, based on theuser-generated target recommendations, the feedback for updating thefirst model and the second model.

As further shown in FIG. 5, process 500 may include causing the firstmodel or the second model to be updated (block 560). For example, thedevice may cause the first model or the second model to be updated, asdescribed above. In some implementations, process 500 may includecausing the first model and/or the second model to be updated orimplemented in the content delivery system based on the feedback, thefirst performance score of the first model, and/or the secondperformance score of the second model. Causing the first model and/orthe second model to be updated or implemented in the content deliverysystem may include causing the first model and/or the second model to beupdated based on the feedback, the first performance score of the firstmodel, and/or the second performance score of the second model.

Process 500 may further include processing, within a second testingmode, the second set of metadata associated with the second contentitem, to generate third recommendations from the first model and fourthrecommendations from the second model; providing the second identifierof the second content item, the third recommendations, and the fourthrecommendations to the client devices; receiving, from the clientdevices, one or more first inputs indicating selection of the thirdrecommendations and/or one or more second inputs indicating selection ofthe fourth recommendations; processing, within the second testing mode,the one or more first inputs and/or the one or more second inputs, toupdate the first performance score of the first model and the secondperformance score of the second model. Processing the one or more firstinputs and/or the one or more second inputs may be to provide additionalfeedback to update the first model and the second model. Processing theone or more first inputs and/or the one or more second inputs maycomprise calculating a first conversion value for the first model basedon a number of the one or more first inputs; calculating a secondconversion value for the second model based on a number of the one ormore second inputs, calculating or updating the first performance scoreof the first model based on the first conversion value; and calculatingor updating the second performance score of the second model based onthe second conversion value.

Process 500 may further include processing, within a third testing mode,the third identifier of the third content item, to generate a fifthrecommendation from the first model and a sixth recommendation from thesecond model; provide the third identifier of the third content item,the fifth recommendation, and the sixth recommendation to the clientdevices; receive, from the client devices, one or more third inputsindicating selection of the fifth recommendation and/or one or morefourth inputs indicating selection of the sixth recommendation; process,within the third testing mode, the one or more third inputs and/or theone or more fourth inputs, to update the first performance score and thesecond performance score. Processing the one or more third inputs and/orthe one or more fourth inputs may comprise calculating a firstconversion value for the first model based on a number of the one ormore third inputs; calculating a second conversion value for the secondmodel based on a number of the one or more fourth inputs; updating thefirst performance score of the first model based on the first conversionvalue; and updating the second performance score of the second modelbased on the second conversion value.

Process 500 may further include performing one or more actions based onthe first performance score of the first model or the second performancescore of the second model. In some implementations, process 500 mayfurther include performing one or more additional actions based on thefirst performance score of the second performance score. The one or moreactions may include causing the first model or the second model to beupdated; causing the first model or the second model to be deleted;generating a report; or reevaluating the first model or the secondmodel. In some implementations, the one or more actions may includegenerating the report based on the first performance score of the firstmodel and the second performance score of the second model; andproviding the report for display. The one or more additional actions mayinclude a different one of causing the first model or the second modelto be updated; causing the first model or the second model to bedeleted; generating the report; or reevaluating the first model or thesecond model.

In some implementations, process 500 may include causing the first modelor the second model to be deleted based on the first performance scoreor the second performance score; generating a report relating to thefirst performance score of the first model, the second performance scoreof the second model, and/or the feedback for updating the first modeland the second model; or reevaluating the first model or the secondmodel after the first model or the second model have been updated basedon the feedback, the first performance score of the first model, and/orthe second performance score of the second model. Process 500 mayinclude generating a report based on the first performance score and thesecond performance score and providing the report for display. Process500 may include reevaluating the first model and the second model toupdate the first performance score of the first model and the secondperformance score of the second model; and causing the first model orthe second model to be implemented within the content delivery systembased on the first performance score of the first model and the secondperformance score of the second model.

Although FIG. 5 shows example blocks of process 500, in someimplementations, process 500 may include additional blocks, fewerblocks, different blocks, or differently arranged blocks than thosedepicted in FIG. 5. Additionally, or alternatively, two or more of theblocks of process 500 may be performed in parallel.

The foregoing disclosure provides illustration and description, but isnot intended to be exhaustive or to limit the implementations to theprecise form disclosed. Modifications and variations may be made inlight of the above disclosure or may be acquired from practice of theimplementations.

As used herein, the term “component” is intended to be broadly construedas hardware, firmware, or a combination of hardware and software.

Certain user interfaces have been described herein and/or shown in thefigures. A user interface may include a graphical user interface, anon-graphical user interface, a text-based user interface, and/or thelike. A user interface may provide information for display. In someimplementations, a user may interact with the information, such as byproviding input via an input component of a device that provides theuser interface for display. In some implementations, a user interfacemay be configurable by a device and/or a user (e.g., a user may changethe size of the user interface, information provided via the userinterface, a position of information provided via the user interface,etc.). Additionally, or alternatively, a user interface may bepre-configured to a standard configuration, a specific configurationbased on a type of device on which the user interface is displayed,and/or a set of configurations based on capabilities and/orspecifications associated with a device on which the user interface isdisplayed.

To the extent the aforementioned implementations collect, store, oremploy personal information of individuals, it should be understood thatsuch information shall be used in accordance with all applicable lawsconcerning protection of personal information. Additionally, thecollection, storage, and use of such information can be subject toconsent of the individual to such activity, for example, through wellknown “opt-in” or “opt-out” processes as can be appropriate for thesituation and type of information. Storage and use of personalinformation can be in an appropriately secure manner reflective of thetype of information, for example, through various encryption andanonymization techniques for particularly sensitive information.

It will be apparent that systems and/or methods described herein may beimplemented in different forms of hardware, firmware, and/or acombination of hardware and software. The actual specialized controlhardware or software code used to implement these systems and/or methodsis not limiting of the implementations. Thus, the operation and behaviorof the systems and/or methods are described herein without reference tospecific software code—it being understood that software and hardwarecan be used to implement the systems and/or methods based on thedescription herein.

Even though particular combinations of features are recited in theclaims and/or disclosed in the specification, these combinations are notintended to limit the disclosure of various implementations. In fact,many of these features may be combined in ways not specifically recitedin the claims and/or disclosed in the specification. Although eachdependent claim listed below may directly depend on only one claim, thedisclosure of various implementations includes each dependent claim incombination with every other claim in the claim set.

No element, act, or instruction used herein should be construed ascritical or essential unless explicitly described as such. Also, as usedherein, the articles “a” and “an” are intended to include one or moreitems, and may be used interchangeably with “one or more.” Further, asused herein, the article “the” is intended to include one or more itemsreferenced in connection with the article “the” and may be usedinterchangeably with “the one or more.” Furthermore, as used herein, theterm “set” is intended to include one or more items (e.g., relateditems, unrelated items, a combination of related and unrelated items,etc.), and may be used interchangeably with “one or more.” Where onlyone item is intended, the phrase “only one” or similar language is used.Also, as used herein, the terms “has,” “have,” “having,” or the like areintended to be open-ended terms. Further, the phrase “based on” isintended to mean “based, at least in part, on” unless explicitly statedotherwise. Also, as used herein, the term “or” is intended to beinclusive when used in a series and may be used interchangeably with“and/or,” unless explicitly stated otherwise (e.g., if used incombination with “either” or “only one of”).

What is claimed is:
 1. A method, comprising: processing, by a device andwithin a testing mode, metadata associated with content data to generaterecommendations from one or more models; receiving, by the device,user-generated target recommendations based on the recommendations;processing, by the device, the user-generated target recommendations andthe recommendations to determine a performance score associated with amodel, of the one or more models, and provide feedback for updating themodel; and causing, by the device, the model to be updated based on thefeedback and the performance score.
 2. The method of claim 1, furthercomprising: calculating a conversion value for the model based on anumber of inputs; and updating the performance score based on theconversion value.
 3. The method of claim 1, wherein the model is a firstmodel, and wherein the one or more models include: the first modeltrained based on a first type of metadata associated with the contentdata, and a second model trained based on a second type of metadataassociated with the content data, wherein the first type of metadata isdifferent than the second type of metadata; and wherein processing themetadata associated with the content data to generate therecommendations comprises: processing the metadata to generate therecommendations based on the first type of metadata and the second typeof metadata.
 4. The method of claim 1, wherein processing theuser-generated target recommendations and the recommendations comprises:determining distance metrics associated with the user-generated targetrecommendations and a set of recommendations, of the recommendations,from the model; calculating weighted scores for the set ofrecommendations based on the distance metrics; and calculating theperformance score based on the weighted scores to determine whether tocause the model to be updated.
 5. The method of claim 1, wherein theuser-generated target recommendations include at least one of therecommendations.
 6. The method of claim 1, further comprising:generating a report relating to the performance score of the model. 7.The method of claim 1, further comprising: reevaluating the model afterthe model has been updated based on the feedback and the performancescore associated with the model.
 8. A device, comprising: one or moreprocessors configured to: generate first recommendations from a firstmodel and second recommendations from a second model based on contentdata, wherein the first model is different than the second model;receive user-generated target recommendations based on a combination ofthe first recommendations and the second recommendations; process theuser-generated target recommendations, the first recommendations, andthe second recommendations, to determine a first performance score ofthe first model or a second performance score of the second model; andperform one or more actions based on the first performance score or thesecond performance score.
 9. The device of claim 8, wherein the firstmodel is trained on different types of metadata than the second model.10. The device of claim 8, wherein the one or more actions include oneof: causing the first model or the second model to be updated; causingthe first model or the second model to be deleted; generating a report;or reevaluating the first model or the second model.
 11. The device ofclaim 8, wherein the one or more processors are further configured to:calculate a first conversion value for the first model based on a numberof inputs; and update the first performance score of the first modelbased on the first conversion value.
 12. The device of claim 8, whereinthe one or more processors, when processing the user-generated targetrecommendations, the first recommendations, and the secondrecommendations, are configured to: calculate first scores for the firstrecommendations based on first distance metrics; calculate second scoresfor the second recommendations based on second distance metrics;calculate the first performance score of the first model based on thefirst scores; and calculate the second performance score of the secondmodel based on the second scores.
 13. The device of claim 8, wherein theone or more actions include: generating a report based on the firstperformance score of the first model and the second performance score ofthe second model; and providing the report for display.
 14. The deviceof claim 8, wherein the user-generated target recommendations include atleast one of the first recommendations.
 15. A non-transitorycomputer-readable medium storing instructions, the instructionscomprising: one or more instructions that, when executed by one or moreprocessors, cause the one or more processors to: process, within atesting mode, metadata associated with content data to generaterecommendations from a model; receive user-generated targetrecommendations based on the recommendations; process the user-generatedtarget recommendations and the recommendations to determine aperformance score associated with the model, and provide feedback forupdating the model; and cause the model to be updated based on thefeedback and the performance score.
 16. The non-transitorycomputer-readable medium of claim 15, wherein the one or moreinstructions further cause the one or more processors to: calculate aconversion value for the model based on a number of inputs; and updatethe performance score based on the conversion value.
 17. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions, when processing the user-generated targetrecommendations and the recommendations, cause the one or moreprocessors to: determine distance metrics associated with theuser-generated target recommendations and the recommendations; calculateweighted scores for the recommendations based on the distance metrics;and calculate the performance score for the model based on the weightedscores to determine whether to cause the model to be updated.
 18. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions further cause the one or more processors to: generatea report relating to the performance score of the model.
 19. Thenon-transitory computer-readable medium of claim 15, wherein the one ormore instructions further cause the one or more processors to:reevaluate the model after the model has been updated based on thefeedback and the performance score.
 20. The non-transitorycomputer-readable medium of claim 15, wherein the user-generated targetrecommendations include at least one of the recommendations.